Articles | Volume 12, issue 4
https://doi.org/10.5194/essd-12-2517-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-12-2517-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Apparent ecosystem carbon turnover time: uncertainties and robust features
Max Planck Institute for Biogeochemistry, Hans Knöll Strasse 10, 07745 Jena, Germany
Sujan Koirala
Max Planck Institute for Biogeochemistry, Hans Knöll Strasse 10, 07745 Jena, Germany
Markus Reichstein
Max Planck Institute for Biogeochemistry, Hans Knöll Strasse 10, 07745 Jena, Germany
Martin Thurner
Biodiversity and Climate Research Centre (BiK-F), Senckenberg Gesellschaft für Naturforschung, Senckenberganlage 25, 60325 Frankfurt am Main, Germany
Valerio Avitabile
European Commission, Joint Research Centre, Via E. Fermi 2749, 21027 Ispra, Italy
Maurizio Santoro
Gamma Remote Sensing, 3073 Gümligen, Switzerland
Bernhard Ahrens
Max Planck Institute for Biogeochemistry, Hans Knöll Strasse 10, 07745 Jena, Germany
Ulrich Weber
Max Planck Institute for Biogeochemistry, Hans Knöll Strasse 10, 07745 Jena, Germany
Max Planck Institute for Biogeochemistry, Hans Knöll Strasse 10, 07745 Jena, Germany
Departamento de Ciências e Engenharia do Ambiente, DCEA, Faculdade de Ciências e Tecnologia, FCT, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal
Related authors
No articles found.
Wenli Zhao, Alexander J. Winkler, Markus Reichstein, Rene Orth, and Pierre Gentine
EGUsphere, https://doi.org/10.5194/egusphere-2025-4082, https://doi.org/10.5194/egusphere-2025-4082, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
We used explainable machine learning that incorporates memory effects to study how plants respond to weather and drought. Using data from 90 sites worldwide, we show that memory plays a key role in regulating plant water stress. Forests and savannas rely on longer past conditions than grasslands, reflecting differences in rooting depth and water use. These insights improve our ability to anticipate ecosystem vulnerability as droughts intensify.
Basil Kraft, Jacob A. Nelson, Sophia Walther, Fabian Gans, Ulrich Weber, Gregory Duveiller, Markus Reichstein, Weijie Zhang, Marc Rußwurm, Devis Tuia, Marco Körner, Zayd Hamdi, and Martin Jung
Biogeosciences, 22, 3965–3987, https://doi.org/10.5194/bg-22-3965-2025, https://doi.org/10.5194/bg-22-3965-2025, 2025
Short summary
Short summary
This study evaluates machine learning approaches for upscaling evapotranspiration from the site to the global scale. Sequential models capture temporal dynamics better, especially with precipitation data, but all models show biases in data-scarce regions. Improved upscaling requires richer training data, informed covariate selection, and physical constraints to enhance robustness and reduce extrapolation errors.
Theertha Kariyathan, Ana Bastos, Markus Reichstein, Wouter Peters, and Julia Marshall
Atmos. Chem. Phys., 25, 7863–7878, https://doi.org/10.5194/acp-25-7863-2025, https://doi.org/10.5194/acp-25-7863-2025, 2025
Short summary
Short summary
The carbon uptake period (CUP) is the time period when land absorbs more CO2 than it emits. While atmospheric CO2 mole fraction measurements can be used to assess CUP changes, atmospheric transport and asynchronous timing across regions reduce the accuracy of the estimates. Forward model experiments show that only ~ 50 % of prescribed shifts in CUP timing applied to surface fluxes (ΔCUPNEE) are captured in simulated CO2 mole fraction data at monitoring sites like the Barrow Atmospheric Baseline Observatory.
Zavud Baghirov, Markus Reichstein, Basil Kraft, Bernhard Ahrens, Marco Körner, and Martin Jung
EGUsphere, https://doi.org/10.5194/egusphere-2025-3123, https://doi.org/10.5194/egusphere-2025-3123, 2025
Short summary
Short summary
We introduce a new global model that links how water and carbon move through land ecosystems. By combining process knowledge with artificial intelligence that learns from observations, we model daily changes in vegetation, water and carbon cycle processes. This model outperforms both purely data-driven and traditional process models, especially in dry and tropical regions. This advance could improve current understanding of water-carbon cycle relationships.
Laura Nadolski, Tarek S. El-Madany, Jacob Nelson, Arnaud Carrara, Gerardo Moreno, Richard Nair, Yunpeng Luo, Anke Hildebrandt, Victor Rolo, Markus Reichstein, and Sung-Ching Lee
Biogeosciences, 22, 2935–2958, https://doi.org/10.5194/bg-22-2935-2025, https://doi.org/10.5194/bg-22-2935-2025, 2025
Short summary
Short summary
Semi-arid ecosystems are crucial for Earth's carbon balance and are sensitive to changes in nitrogen (N) and phosphorus (P) levels. Their carbon dynamics are complex and not fully understood. We studied how long-term nutrient changes affect carbon exchange. In summer, the addition of N+P changed plant composition and productivity. In transitional seasons, carbon exchange was less weather-dependent with N. The addition of N and N+P increases carbon-exchange variability, driven by grass greenness.
Min Feng, Joseph O. Sexton, Panshi Wang, Paul M. Montesano, Leonardo Calle, Nuno Carvalhais, Benjamin Poulter, Matthew J. Macander, Michael A. Wulder, Margaret Wooten, William Wagner, Akiko Elders, Saurabh Channan, and Christopher S. R. Neigh
EGUsphere, https://doi.org/10.5194/egusphere-2025-2268, https://doi.org/10.5194/egusphere-2025-2268, 2025
Short summary
Short summary
The boreal forest, warming fastest among forested biomes, shows a northward shift in tree cover. Using the longest, highest-resolution satellite maps, we found an 0.844 million km² increase in tree cover and a 0.45° northward shift from 1985–2020, especially in northern latitudes. Stable disturbance rates suggest climate-driven growth. Young forests' biomass may help reduce global CO2, despite uncertainties in carbon balance, disturbance, and respiration.
Friedrich J. Bohn, Ana Bastos, Romina Martin, Anja Rammig, Niak Sian Koh, Giles B. Sioen, Bram Buscher, Louise Carver, Fabrice DeClerck, Moritz Drupp, Robert Fletcher, Matthew Forrest, Alexandros Gasparatos, Alex Godoy-Faúndez, Gregor Hagedorn, Martin C. Hänsel, Jessica Hetzer, Thomas Hickler, Cornelia B. Krug, Stasja Koot, Xiuzhen Li, Amy Luers, Shelby Matevich, H. Damon Matthews, Ina C. Meier, Mirco Migliavacca, Awaz Mohamed, Sungmin O, David Obura, Ben Orlove, Rene Orth, Laura Pereira, Markus Reichstein, Lerato Thakholi, Peter H. Verburg, and Yuki Yoshida
Biogeosciences, 22, 2425–2460, https://doi.org/10.5194/bg-22-2425-2025, https://doi.org/10.5194/bg-22-2425-2025, 2025
Short summary
Short summary
An interdisciplinary collaboration of 36 international researchers from 35 institutions highlights recent findings in biosphere research. Within eight themes, they discuss issues arising from climate change and other anthropogenic stressors and highlight the co-benefits of nature-based solutions and ecosystem services. Based on an analysis of these eight topics, we have synthesized four overarching insights.
Samuel Upton, Markus Reichstein, Wouter Peters, Santiago Botía, Jacob A. Nelson, Sophia Walther, Martin Jung, Fabian Gans, László Haszpra, and Ana Bastos
EGUsphere, https://doi.org/10.5194/egusphere-2025-2097, https://doi.org/10.5194/egusphere-2025-2097, 2025
Short summary
Short summary
We create a hybrid ecosystem-level carbon flux model using both eddy-covariance observations and observations of the atmospheric mole fraction of CO2 at three tall-tower observatories. Our study uses an atmospheric transport model (STILT) to connect the atmospheric signal to the ecosystem-level model. We show that this inclusion of atmospheric information meaningfully improves the model's representation of the interannual variability of the global net flux of CO2.
Zavud Baghirov, Martin Jung, Markus Reichstein, Marco Körner, and Basil Kraft
Geosci. Model Dev., 18, 2921–2943, https://doi.org/10.5194/gmd-18-2921-2025, https://doi.org/10.5194/gmd-18-2921-2025, 2025
Short summary
Short summary
We use an innovative approach to studying the Earth's water cycle by integrating advanced machine learning techniques with a traditional water cycle model. Our model is designed to learn from observational data, with a particular emphasis on understanding the influence of vegetation on water movement. By closely aligning with real-world observations, our model offers new possibilities for enhancing our understanding of the water cycle and its interactions with vegetation.
Na Li, Sebastian Sippel, Nora Linscheid, Miguel D. Mahecha, Markus Reichstein, and Ana Bastos
EGUsphere, https://doi.org/10.5194/egusphere-2025-1924, https://doi.org/10.5194/egusphere-2025-1924, 2025
Short summary
Short summary
The global land carbon sink has increased since the pre-industrial period, mainly caused by increasing atmospheric CO2 emissions and climate change. However, the large year-to-year variations can mask or amplify this trend. Here, we detect the time for the anthropogenic signal to emerge over natural variations in land carbon sink. We removed the circulation-induced variations in the global land carbon sink and effectively reduced the detection time of anthropogenic signal.
Marleen Pallandt, Marion Schrumpf, Holger Lange, Markus Reichstein, Lin Yu, and Bernhard Ahrens
Biogeosciences, 22, 1907–1928, https://doi.org/10.5194/bg-22-1907-2025, https://doi.org/10.5194/bg-22-1907-2025, 2025
Short summary
Short summary
As soils warm due to climate change, soil organic carbon (SOC) decomposes faster due to increased microbial activity, given sufficient available moisture. We modelled the microbial decomposition of plant litter and residue at different depths and found that deep soil layers are more sensitive than topsoils. Warming causes SOC loss, but its extent depends on the litter type and its temperature sensitivity, which can either counteract or amplify losses. Droughts may also counteract warming-induced SOC losses.
Martin Thurner, Kailiang Yu, Stefano Manzoni, Anatoly Prokushkin, Melanie A. Thurner, Zhiqiang Wang, and Thomas Hickler
Biogeosciences, 22, 1475–1493, https://doi.org/10.5194/bg-22-1475-2025, https://doi.org/10.5194/bg-22-1475-2025, 2025
Short summary
Short summary
Nitrogen concentrations in tree tissues (leaves, branches, stems, and roots) are related to photosynthesis, growth, and respiration and thus to vegetation carbon uptake. Our novel database allows us to identify the controls of tree tissue nitrogen concentrations in boreal and temperate forests, such as tree age/size, species, and climate. Changes therein will affect tissue nitrogen concentrations and thus also vegetation carbon uptake.
Wenli Zhao, Alexander J. Winkler, Markus Reichstein, Rene Orth, and Pierre Gentine
EGUsphere, https://doi.org/10.5194/egusphere-2025-365, https://doi.org/10.5194/egusphere-2025-365, 2025
Preprint archived
Short summary
Short summary
We developed a machine learning model that accounts for the memory effects of soil moisture and vegetation to predict Evaporative Fraction (EF) without relying on soil moisture as a direct input. The model accurately predicts EF during dry periods for the unseen sites, highlighting the key of meteorological memory effects. The learned memory effect related to rooting depth and soil water holding capacity could potentially serve as proxies for assessing the plant water stress.
Jacob A. Nelson, Sophia Walther, Fabian Gans, Basil Kraft, Ulrich Weber, Kimberly Novick, Nina Buchmann, Mirco Migliavacca, Georg Wohlfahrt, Ladislav Šigut, Andreas Ibrom, Dario Papale, Mathias Göckede, Gregory Duveiller, Alexander Knohl, Lukas Hörtnagl, Russell L. Scott, Jiří Dušek, Weijie Zhang, Zayd Mahmoud Hamdi, Markus Reichstein, Sergio Aranda-Barranco, Jonas Ardö, Maarten Op de Beeck, Dave Billesbach, David Bowling, Rosvel Bracho, Christian Brümmer, Gustau Camps-Valls, Shiping Chen, Jamie Rose Cleverly, Ankur Desai, Gang Dong, Tarek S. El-Madany, Eugenie Susanne Euskirchen, Iris Feigenwinter, Marta Galvagno, Giacomo A. Gerosa, Bert Gielen, Ignacio Goded, Sarah Goslee, Christopher Michael Gough, Bernard Heinesch, Kazuhito Ichii, Marcin Antoni Jackowicz-Korczynski, Anne Klosterhalfen, Sara Knox, Hideki Kobayashi, Kukka-Maaria Kohonen, Mika Korkiakoski, Ivan Mammarella, Mana Gharun, Riccardo Marzuoli, Roser Matamala, Stefan Metzger, Leonardo Montagnani, Giacomo Nicolini, Thomas O'Halloran, Jean-Marc Ourcival, Matthias Peichl, Elise Pendall, Borja Ruiz Reverter, Marilyn Roland, Simone Sabbatini, Torsten Sachs, Marius Schmidt, Christopher R. Schwalm, Ankit Shekhar, Richard Silberstein, Maria Lucia Silveira, Donatella Spano, Torbern Tagesson, Gianluca Tramontana, Carlo Trotta, Fabio Turco, Timo Vesala, Caroline Vincke, Domenico Vitale, Enrique R. Vivoni, Yi Wang, William Woodgate, Enrico A. Yepez, Junhui Zhang, Donatella Zona, and Martin Jung
Biogeosciences, 21, 5079–5115, https://doi.org/10.5194/bg-21-5079-2024, https://doi.org/10.5194/bg-21-5079-2024, 2024
Short summary
Short summary
The movement of water, carbon, and energy from the Earth's surface to the atmosphere, or flux, is an important process to understand because it impacts our lives. Here, we outline a method called FLUXCOM-X to estimate global water and CO2 fluxes based on direct measurements from sites around the world. We go on to demonstrate how these new estimates of net CO2 uptake/loss, gross CO2 uptake, total water evaporation, and transpiration from plants compare to previous and independent estimates.
Mélanie Weynants, Chaonan Ji, Nora Linscheid, Ulrich Weber, Miguel D. Mahecha, and Fabian Gans
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-396, https://doi.org/10.5194/essd-2024-396, 2024
Preprint under review for ESSD
Short summary
Short summary
Climate extremes are intensifying. The impacts of heatwaves and droughts can be made worse when they happen at the same time. Dheed is a global database of dry and hot compound extreme events from 1950 to 2022. It can be combined with other data to study the impacts of those events on terrestrial ecosystems, specific species or human societies. Dheed's analysis confirms that extremely dry and hot days have become more common on all continents in recent decades, especially in Europe and Africa.
Guohua Liu, Mirco Migliavacca, Christian Reimers, Basil Kraft, Markus Reichstein, Andrew D. Richardson, Lisa Wingate, Nicolas Delpierre, Hui Yang, and Alexander J. Winkler
Geosci. Model Dev., 17, 6683–6701, https://doi.org/10.5194/gmd-17-6683-2024, https://doi.org/10.5194/gmd-17-6683-2024, 2024
Short summary
Short summary
Our study employs long short-term memory (LSTM) networks to model canopy greenness and phenology, integrating meteorological memory effects. The LSTM model outperforms traditional methods, enhancing accuracy in predicting greenness dynamics and phenological transitions across plant functional types. Highlighting the importance of multi-variate meteorological memory effects, our research pioneers unlock the secrets of vegetation phenology responses to climate change with deep learning techniques.
Jasper M. C. Denissen, Adriaan J. Teuling, Sujan Koirala, Markus Reichstein, Gianpaolo Balsamo, Martha M. Vogel, Xin Yu, and René Orth
Earth Syst. Dynam., 15, 717–734, https://doi.org/10.5194/esd-15-717-2024, https://doi.org/10.5194/esd-15-717-2024, 2024
Short summary
Short summary
Heat extremes have severe implications for human health and ecosystems. Heat extremes are mostly introduced by large-scale atmospheric circulation but can be modulated by vegetation. Vegetation with access to water uses solar energy to evaporate water into the atmosphere. Under dry conditions, water may not be available, suppressing evaporation and heating the atmosphere. Using climate projections, we show that regionally less water is available for vegetation, intensifying future heat extremes.
Bjorn Stevens, Stefan Adami, Tariq Ali, Hartwig Anzt, Zafer Aslan, Sabine Attinger, Jaana Bäck, Johanna Baehr, Peter Bauer, Natacha Bernier, Bob Bishop, Hendryk Bockelmann, Sandrine Bony, Guy Brasseur, David N. Bresch, Sean Breyer, Gilbert Brunet, Pier Luigi Buttigieg, Junji Cao, Christelle Castet, Yafang Cheng, Ayantika Dey Choudhury, Deborah Coen, Susanne Crewell, Atish Dabholkar, Qing Dai, Francisco Doblas-Reyes, Dale Durran, Ayoub El Gaidi, Charlie Ewen, Eleftheria Exarchou, Veronika Eyring, Florencia Falkinhoff, David Farrell, Piers M. Forster, Ariane Frassoni, Claudia Frauen, Oliver Fuhrer, Shahzad Gani, Edwin Gerber, Debra Goldfarb, Jens Grieger, Nicolas Gruber, Wilco Hazeleger, Rolf Herken, Chris Hewitt, Torsten Hoefler, Huang-Hsiung Hsu, Daniela Jacob, Alexandra Jahn, Christian Jakob, Thomas Jung, Christopher Kadow, In-Sik Kang, Sarah Kang, Karthik Kashinath, Katharina Kleinen-von Königslöw, Daniel Klocke, Uta Kloenne, Milan Klöwer, Chihiro Kodama, Stefan Kollet, Tobias Kölling, Jenni Kontkanen, Steve Kopp, Michal Koran, Markku Kulmala, Hanna Lappalainen, Fakhria Latifi, Bryan Lawrence, June Yi Lee, Quentin Lejeun, Christian Lessig, Chao Li, Thomas Lippert, Jürg Luterbacher, Pekka Manninen, Jochem Marotzke, Satoshi Matsouoka, Charlotte Merchant, Peter Messmer, Gero Michel, Kristel Michielsen, Tomoki Miyakawa, Jens Müller, Ramsha Munir, Sandeep Narayanasetti, Ousmane Ndiaye, Carlos Nobre, Achim Oberg, Riko Oki, Tuba Özkan-Haller, Tim Palmer, Stan Posey, Andreas Prein, Odessa Primus, Mike Pritchard, Julie Pullen, Dian Putrasahan, Johannes Quaas, Krishnan Raghavan, Venkatachalam Ramaswamy, Markus Rapp, Florian Rauser, Markus Reichstein, Aromar Revi, Sonakshi Saluja, Masaki Satoh, Vera Schemann, Sebastian Schemm, Christina Schnadt Poberaj, Thomas Schulthess, Cath Senior, Jagadish Shukla, Manmeet Singh, Julia Slingo, Adam Sobel, Silvina Solman, Jenna Spitzer, Philip Stier, Thomas Stocker, Sarah Strock, Hang Su, Petteri Taalas, John Taylor, Susann Tegtmeier, Georg Teutsch, Adrian Tompkins, Uwe Ulbrich, Pier-Luigi Vidale, Chien-Ming Wu, Hao Xu, Najibullah Zaki, Laure Zanna, Tianjun Zhou, and Florian Ziemen
Earth Syst. Sci. Data, 16, 2113–2122, https://doi.org/10.5194/essd-16-2113-2024, https://doi.org/10.5194/essd-16-2113-2024, 2024
Short summary
Short summary
To manage Earth in the Anthropocene, new tools, new institutions, and new forms of international cooperation will be required. Earth Virtualization Engines is proposed as an international federation of centers of excellence to empower all people to respond to the immense and urgent challenges posed by climate change.
Sinikka J. Paulus, Rene Orth, Sung-Ching Lee, Anke Hildebrandt, Martin Jung, Jacob A. Nelson, Tarek Sebastian El-Madany, Arnaud Carrara, Gerardo Moreno, Matthias Mauder, Jannis Groh, Alexander Graf, Markus Reichstein, and Mirco Migliavacca
Biogeosciences, 21, 2051–2085, https://doi.org/10.5194/bg-21-2051-2024, https://doi.org/10.5194/bg-21-2051-2024, 2024
Short summary
Short summary
Porous materials are known to reversibly trap water from the air, even at low humidity. However, this behavior is poorly understood for soils. In this analysis, we test whether eddy covariance is able to measure the so-called adsorption of atmospheric water vapor by soils. We find that this flux occurs frequently during dry nights in a Mediterranean ecosystem, while EC detects downwardly directed vapor fluxes. These results can help to map moisture uptake globally.
Martin Jung, Jacob Nelson, Mirco Migliavacca, Tarek El-Madany, Dario Papale, Markus Reichstein, Sophia Walther, and Thomas Wutzler
Biogeosciences, 21, 1827–1846, https://doi.org/10.5194/bg-21-1827-2024, https://doi.org/10.5194/bg-21-1827-2024, 2024
Short summary
Short summary
We present a methodology to detect inconsistencies in perhaps the most important data source for measurements of ecosystem–atmosphere carbon, water, and energy fluxes. We expect that the derived consistency flags will be relevant for data users and will help in improving our understanding of and our ability to model ecosystem–climate interactions.
Thomas Wutzler, Christian Reimers, Bernhard Ahrens, and Marion Schrumpf
Geosci. Model Dev., 17, 2705–2725, https://doi.org/10.5194/gmd-17-2705-2024, https://doi.org/10.5194/gmd-17-2705-2024, 2024
Short summary
Short summary
Soil microbes provide a strong link for elemental fluxes in the earth system. The SESAM model applies an optimality assumption to model those linkages and their adaptation. We found that a previous heuristic description was a special case of a newly developed more rigorous description. The finding of new behaviour at low microbial biomass led us to formulate the constrained enzyme hypothesis. We now can better describe how microbially mediated linkages of elemental fluxes adapt across decades.
Samuel Upton, Markus Reichstein, Fabian Gans, Wouter Peters, Basil Kraft, and Ana Bastos
Atmos. Chem. Phys., 24, 2555–2582, https://doi.org/10.5194/acp-24-2555-2024, https://doi.org/10.5194/acp-24-2555-2024, 2024
Short summary
Short summary
Data-driven eddy-covariance upscaled estimates of the global land–atmosphere net CO2 exchange (NEE) show important mismatches with regional and global estimates based on atmospheric information. To address this, we create a model with a dual constraint based on bottom-up eddy-covariance data and top-down atmospheric inversion data. Our model overcomes shortcomings of each approach, producing improved NEE estimates from local to global scale, helping to reduce uncertainty in the carbon budget.
Hui Yang, Krzysztof Stereńczak, Zbigniew Karaszewski, and Nuno Carvalhais
EGUsphere, https://doi.org/10.5194/egusphere-2023-2691, https://doi.org/10.5194/egusphere-2023-2691, 2023
Short summary
Short summary
Wood density is crucial for ecological and carbon stock assessment, yet its labor-intensive analysis limits studies across species and spaces. Our study, based on 48,000 samples from Central Europe, reveals that, even without species information, 91% of inter-tree variations can be predicted by vegetation indexes, topography, and soil texture. Importantly, we highlight neglected intra-tree variation, showing substantial variations vertically along the height and radially from the center to bark.
Richard Nair, Yunpeng Luo, Tarek El-Madany, Victor Rolo, Javier Pacheco-Labrador, Silvia Caldararu, Kendalynn A. Morris, Marion Schrumpf, Arnaud Carrara, Gerardo Moreno, Markus Reichstein, and Mirco Migliavacca
EGUsphere, https://doi.org/10.5194/egusphere-2023-2434, https://doi.org/10.5194/egusphere-2023-2434, 2023
Preprint archived
Short summary
Short summary
We studied a Mediterranean ecosystem to understand carbon uptake efficiency and its controls. These ecosystems face potential nitrogen-phosphorus imbalances due to pollution. Analysing six years of carbon data, we assessed controls at different timeframes. This is crucial for predicting such vulnerable regions. Our findings revealed N limitation on C uptake, not N:P imbalance, and strong influence of water availability. whether drought or wetness promoted net C uptake depended on timescale.
Theertha Kariyathan, Ana Bastos, Julia Marshall, Wouter Peters, Pieter Tans, and Markus Reichstein
Atmos. Meas. Tech., 16, 3299–3312, https://doi.org/10.5194/amt-16-3299-2023, https://doi.org/10.5194/amt-16-3299-2023, 2023
Short summary
Short summary
The timing and duration of the carbon uptake period (CUP) are sensitive to the occurrence of major phenological events, which are influenced by recent climate change. This study presents an ensemble-based approach for quantifying the timing and duration of the CUP and their uncertainty when derived from atmospheric CO2 measurements with noise and gaps. The CUP metrics derived with the approach are more robust and have less uncertainty than when estimated with the conventional methods.
Hoontaek Lee, Martin Jung, Nuno Carvalhais, Tina Trautmann, Basil Kraft, Markus Reichstein, Matthias Forkel, and Sujan Koirala
Hydrol. Earth Syst. Sci., 27, 1531–1563, https://doi.org/10.5194/hess-27-1531-2023, https://doi.org/10.5194/hess-27-1531-2023, 2023
Short summary
Short summary
We spatially attribute the variance in global terrestrial water storage (TWS) interannual variability (IAV) and its modeling error with two data-driven hydrological models. We find error hotspot regions that show a disproportionately large significance in the global mismatch and the association of the error regions with a smaller-scale lateral convergence of water. Our findings imply that TWS IAV modeling can be efficiently improved by focusing on model representations for the error hotspots.
Robert Vautard, Geert Jan van Oldenborgh, Rémy Bonnet, Sihan Li, Yoann Robin, Sarah Kew, Sjoukje Philip, Jean-Michel Soubeyroux, Brigitte Dubuisson, Nicolas Viovy, Markus Reichstein, Friederike Otto, and Iñaki Garcia de Cortazar-Atauri
Nat. Hazards Earth Syst. Sci., 23, 1045–1058, https://doi.org/10.5194/nhess-23-1045-2023, https://doi.org/10.5194/nhess-23-1045-2023, 2023
Short summary
Short summary
A deep frost occurred in early April 2021, inducing severe damages in grapevine and fruit trees in France. We found that such extreme frosts occurring after the start of the growing season such as those of April 2021 are currently about 2°C colder [0.5 °C to 3.3 °C] in observations than in preindustrial climate. This observed intensification of growing-period frosts is attributable, at least in part, to human-caused climate change, making the 2021 event 50 % more likely [10 %–110 %].
Lin Yu, Silvia Caldararu, Bernhard Ahrens, Thomas Wutzler, Marion Schrumpf, Julian Helfenstein, Chiara Pistocchi, and Sönke Zaehle
Biogeosciences, 20, 57–73, https://doi.org/10.5194/bg-20-57-2023, https://doi.org/10.5194/bg-20-57-2023, 2023
Short summary
Short summary
In this study, we addressed a key weakness in current ecosystem models regarding the phosphorus exchange in the soil and developed a new scheme to describe this process. We showed that the new scheme improved the model performance for plant productivity, soil organic carbon, and soil phosphorus content at five beech forest sites in Germany. We claim that this new model could be used as a better tool to study ecosystems under future climate change, particularly phosphorus-limited systems.
Sinikka Jasmin Paulus, Tarek Sebastian El-Madany, René Orth, Anke Hildebrandt, Thomas Wutzler, Arnaud Carrara, Gerardo Moreno, Oscar Perez-Priego, Olaf Kolle, Markus Reichstein, and Mirco Migliavacca
Hydrol. Earth Syst. Sci., 26, 6263–6287, https://doi.org/10.5194/hess-26-6263-2022, https://doi.org/10.5194/hess-26-6263-2022, 2022
Short summary
Short summary
In this study, we analyze small inputs of water to ecosystems such as fog, dew, and adsorption of vapor. To measure them, we use a scaling system and later test our attribution of different water fluxes to weight changes. We found that they occur frequently during 1 year in a dry summer ecosystem. In each season, a different flux seems dominant, but they all mainly occur during the night. Therefore, they could be important for the biosphere because rain is unevenly distributed over the year.
Na Li, Sebastian Sippel, Alexander J. Winkler, Miguel D. Mahecha, Markus Reichstein, and Ana Bastos
Earth Syst. Dynam., 13, 1505–1533, https://doi.org/10.5194/esd-13-1505-2022, https://doi.org/10.5194/esd-13-1505-2022, 2022
Short summary
Short summary
Quantifying the imprint of large-scale atmospheric circulation dynamics and associated carbon cycle responses is key to improving our understanding of carbon cycle dynamics. Using a statistical model that relies on spatiotemporal sea level pressure as a proxy for large-scale atmospheric circulation, we quantify the fraction of interannual variability in atmospheric CO2 growth rate and the land CO2 sink that are driven by atmospheric circulation variability.
Melissa Ruiz-Vásquez, Sungmin O, Alexander Brenning, Randal D. Koster, Gianpaolo Balsamo, Ulrich Weber, Gabriele Arduini, Ana Bastos, Markus Reichstein, and René Orth
Earth Syst. Dynam., 13, 1451–1471, https://doi.org/10.5194/esd-13-1451-2022, https://doi.org/10.5194/esd-13-1451-2022, 2022
Short summary
Short summary
Subseasonal forecasts facilitate early warning of extreme events; however their predictability sources are not fully explored. We find that global temperature forecast errors in many regions are related to climate variables such as solar radiation and precipitation, as well as land surface variables such as soil moisture and evaporative fraction. A better representation of these variables in the forecasting and data assimilation systems can support the accuracy of temperature forecasts.
Xin Yu, René Orth, Markus Reichstein, Michael Bahn, Anne Klosterhalfen, Alexander Knohl, Franziska Koebsch, Mirco Migliavacca, Martina Mund, Jacob A. Nelson, Benjamin D. Stocker, Sophia Walther, and Ana Bastos
Biogeosciences, 19, 4315–4329, https://doi.org/10.5194/bg-19-4315-2022, https://doi.org/10.5194/bg-19-4315-2022, 2022
Short summary
Short summary
Identifying drought legacy effects is challenging because they are superimposed on variability driven by climate conditions in the recovery period. We develop a residual-based approach to quantify legacies on gross primary productivity (GPP) from eddy covariance data. The GPP reduction due to legacy effects is comparable to the concurrent effects at two sites in Germany, which reveals the importance of legacy effects. Our novel methodology can be used to quantify drought legacies elsewhere.
Sophia Walther, Simon Besnard, Jacob Allen Nelson, Tarek Sebastian El-Madany, Mirco Migliavacca, Ulrich Weber, Nuno Carvalhais, Sofia Lorena Ermida, Christian Brümmer, Frederik Schrader, Anatoly Stanislavovich Prokushkin, Alexey Vasilevich Panov, and Martin Jung
Biogeosciences, 19, 2805–2840, https://doi.org/10.5194/bg-19-2805-2022, https://doi.org/10.5194/bg-19-2805-2022, 2022
Short summary
Short summary
Satellite observations help interpret station measurements of local carbon, water, and energy exchange between the land surface and the atmosphere and are indispensable for simulations of the same in land surface models and their evaluation. We propose generalisable and efficient approaches to systematically ensure high quality and to estimate values in data gaps. We apply them to satellite data of surface reflectance and temperature with different resolutions at the stations.
Philip J. Ward, James Daniell, Melanie Duncan, Anna Dunne, Cédric Hananel, Stefan Hochrainer-Stigler, Annegien Tijssen, Silvia Torresan, Roxana Ciurean, Joel C. Gill, Jana Sillmann, Anaïs Couasnon, Elco Koks, Noemi Padrón-Fumero, Sharon Tatman, Marianne Tronstad Lund, Adewole Adesiyun, Jeroen C. J. H. Aerts, Alexander Alabaster, Bernard Bulder, Carlos Campillo Torres, Andrea Critto, Raúl Hernández-Martín, Marta Machado, Jaroslav Mysiak, Rene Orth, Irene Palomino Antolín, Eva-Cristina Petrescu, Markus Reichstein, Timothy Tiggeloven, Anne F. Van Loon, Hung Vuong Pham, and Marleen C. de Ruiter
Nat. Hazards Earth Syst. Sci., 22, 1487–1497, https://doi.org/10.5194/nhess-22-1487-2022, https://doi.org/10.5194/nhess-22-1487-2022, 2022
Short summary
Short summary
The majority of natural-hazard risk research focuses on single hazards (a flood, a drought, a volcanic eruption, an earthquake, etc.). In the international research and policy community it is recognised that risk management could benefit from a more systemic approach. In this perspective paper, we argue for an approach that addresses multi-hazard, multi-risk management through the lens of sustainability challenges that cut across sectors, regions, and hazards.
Basil Kraft, Martin Jung, Marco Körner, Sujan Koirala, and Markus Reichstein
Hydrol. Earth Syst. Sci., 26, 1579–1614, https://doi.org/10.5194/hess-26-1579-2022, https://doi.org/10.5194/hess-26-1579-2022, 2022
Short summary
Short summary
We present a physics-aware machine learning model of the global hydrological cycle. As the model uses neural networks under the hood, the simulations of the water cycle are learned from data, and yet they are informed and constrained by physical knowledge. The simulated patterns lie within the range of existing hydrological models and are plausible. The hybrid modeling approach has the potential to tackle key environmental questions from a novel perspective.
Tina Trautmann, Sujan Koirala, Nuno Carvalhais, Andreas Güntner, and Martin Jung
Hydrol. Earth Syst. Sci., 26, 1089–1109, https://doi.org/10.5194/hess-26-1089-2022, https://doi.org/10.5194/hess-26-1089-2022, 2022
Short summary
Short summary
We assess the effect of how vegetation is defined in a global hydrological model on the composition of total water storage (TWS). We compare two experiments, one with globally uniform and one with vegetation parameters that vary in space and time. While both experiments are constrained against observational data, we found a drastic change in the partitioning of TWS, highlighting the important role of the interaction between groundwater–soil moisture–vegetation in understanding TWS variations.
J. Pacheco-Labrador, U. Weber, X. Ma, M. D. Mahecha, N. Carvalhais, C. Wirth, A. Huth, F. J. Bohn, G. Kraemer, U. Heiden, FunDivEUROPE members, and M. Migliavacca
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-1-W1-2021, 49–55, https://doi.org/10.5194/isprs-archives-XLVI-1-W1-2021-49-2022, https://doi.org/10.5194/isprs-archives-XLVI-1-W1-2021-49-2022, 2022
Simon Besnard, Sujan Koirala, Maurizio Santoro, Ulrich Weber, Jacob Nelson, Jonas Gütter, Bruno Herault, Justin Kassi, Anny N'Guessan, Christopher Neigh, Benjamin Poulter, Tao Zhang, and Nuno Carvalhais
Earth Syst. Sci. Data, 13, 4881–4896, https://doi.org/10.5194/essd-13-4881-2021, https://doi.org/10.5194/essd-13-4881-2021, 2021
Short summary
Short summary
Forest age can determine the capacity of a forest to uptake carbon from the atmosphere. Yet, a lack of global diagnostics that reflect the forest stage and associated disturbance regimes hampers the quantification of age-related differences in forest carbon dynamics. In this paper, we introduced a new global distribution of forest age inferred from forest inventory, remote sensing and climate data in support of a better understanding of the global dynamics in the forest water and carbon cycles.
Ana Bastos, René Orth, Markus Reichstein, Philippe Ciais, Nicolas Viovy, Sönke Zaehle, Peter Anthoni, Almut Arneth, Pierre Gentine, Emilie Joetzjer, Sebastian Lienert, Tammas Loughran, Patrick C. McGuire, Sungmin O, Julia Pongratz, and Stephen Sitch
Earth Syst. Dynam., 12, 1015–1035, https://doi.org/10.5194/esd-12-1015-2021, https://doi.org/10.5194/esd-12-1015-2021, 2021
Short summary
Short summary
Temperate biomes in Europe are not prone to recurrent dry and hot conditions in summer. However, these conditions may become more frequent in the coming decades. Because stress conditions can leave legacies for many years, this may result in reduced ecosystem resilience under recurrent stress. We assess vegetation vulnerability to the hot and dry summers in 2018 and 2019 in Europe and find the important role of inter-annual legacy effects from 2018 in modulating the impacts of the 2019 event.
Yeonuk Kim, Monica Garcia, Laura Morillas, Ulrich Weber, T. Andrew Black, and Mark S. Johnson
Hydrol. Earth Syst. Sci., 25, 5175–5191, https://doi.org/10.5194/hess-25-5175-2021, https://doi.org/10.5194/hess-25-5175-2021, 2021
Short summary
Short summary
Here, we present a novel physically based evaporation model to demonstrate that vertical relative humidity (RH) gradients from the land surface to the atmosphere tend to evolve towards zero due to land–atmosphere equilibration processes. Collapsing RH gradients on daily to yearly timescales indicate an emergent land–atmosphere equilibrium, making it possible to determine evapotranspiration using only meteorological information, independent of land surface conditions and vegetation controls.
Yuanyuan Huang, Phillipe Ciais, Maurizio Santoro, David Makowski, Jerome Chave, Dmitry Schepaschenko, Rose Z. Abramoff, Daniel S. Goll, Hui Yang, Ye Chen, Wei Wei, and Shilong Piao
Earth Syst. Sci. Data, 13, 4263–4274, https://doi.org/10.5194/essd-13-4263-2021, https://doi.org/10.5194/essd-13-4263-2021, 2021
Short summary
Short summary
Roots play a key role in our Earth system. Here we combine 10 307 field measurements of forest root biomass worldwide with global observations of forest structure, climatic conditions, topography, land management and soil characteristics to derive a spatially explicit global high-resolution (~ 1 km) root biomass dataset. In total, 142 ± 25 (95 % CI) Pg of live dry-matter biomass is stored belowground, representing a global average root : shoot biomass ratio of 0.25 ± 0.10.
Maurizio Santoro, Oliver Cartus, Nuno Carvalhais, Danaë M. A. Rozendaal, Valerio Avitabile, Arnan Araza, Sytze de Bruin, Martin Herold, Shaun Quegan, Pedro Rodríguez-Veiga, Heiko Balzter, João Carreiras, Dmitry Schepaschenko, Mikhail Korets, Masanobu Shimada, Takuya Itoh, Álvaro Moreno Martínez, Jura Cavlovic, Roberto Cazzolla Gatti, Polyanna da Conceição Bispo, Nasheta Dewnath, Nicolas Labrière, Jingjing Liang, Jeremy Lindsell, Edward T. A. Mitchard, Alexandra Morel, Ana Maria Pacheco Pascagaza, Casey M. Ryan, Ferry Slik, Gaia Vaglio Laurin, Hans Verbeeck, Arief Wijaya, and Simon Willcock
Earth Syst. Sci. Data, 13, 3927–3950, https://doi.org/10.5194/essd-13-3927-2021, https://doi.org/10.5194/essd-13-3927-2021, 2021
Short summary
Short summary
Forests play a crucial role in Earth’s carbon cycle. To understand the carbon cycle better, we generated a global dataset of forest above-ground biomass, i.e. carbon stocks, from satellite data of 2010. This dataset provides a comprehensive and detailed portrait of the distribution of carbon in forests, although for dense forests in the tropics values are somewhat underestimated. This dataset will have a considerable impact on climate, carbon, and socio-economic modelling schemes.
Christopher Krich, Mirco Migliavacca, Diego G. Miralles, Guido Kraemer, Tarek S. El-Madany, Markus Reichstein, Jakob Runge, and Miguel D. Mahecha
Biogeosciences, 18, 2379–2404, https://doi.org/10.5194/bg-18-2379-2021, https://doi.org/10.5194/bg-18-2379-2021, 2021
Short summary
Short summary
Ecosystems and the atmosphere interact with each other. These interactions determine e.g. the water and carbon fluxes and thus are crucial to understand climate change effects. We analysed the interactions for many ecosystems across the globe, showing that very different ecosystems can have similar interactions with the atmosphere. Meteorological conditions seem to be the strongest interaction-shaping factor. This means that common principles can be identified to describe ecosystem behaviour.
Milan Flach, Alexander Brenning, Fabian Gans, Markus Reichstein, Sebastian Sippel, and Miguel D. Mahecha
Biogeosciences, 18, 39–53, https://doi.org/10.5194/bg-18-39-2021, https://doi.org/10.5194/bg-18-39-2021, 2021
Short summary
Short summary
Drought and heat events affect the uptake and sequestration of carbon in terrestrial ecosystems. We study the impact of droughts and heatwaves on the uptake of CO2 of different vegetation types at the global scale. We find that agricultural areas are generally strongly affected. Forests instead are not particularly sensitive to the events under scrutiny. This implies different water management strategies of forests but also a lack of sensitivity to remote-sensing-derived vegetation activity.
Cited articles
Avitabile, V., Herold, M., Heuvelink, G., Lewis, S. L., Phillips, O. L., Asner, G. P.,
Armston, J., Ashton, P. S., Banin, L., and Bayol, N.: An integrated pan-tropical biomass map using
multiple reference datasets, Glob. Change Biol., 22, 1406–1420, https://doi.org/10.1111/gcb.13139, 2016.
Baccini, A., Goetz, S., Walker, W., Laporte, N., Sun, M., Sulla-Menashe, D., Hackler,
J., Beck, P., Dubayah, R., and Friedl, M.: Estimated carbon dioxide emissions from tropical
deforestation improved by carbon-density maps, Nat. Clim. Change, 2, 182–185, 2012.
Barrett, D. J.: Steady state turnover time of carbon in the Australian terrestrial
biosphere, Global Biogeochem. Cy., 16, 55-1–55-21, 2002.
Batjes, N.: Harmonized soil property values for broad-scale modelling (WISE30sec) with
estimates of global soil carbon stocks, Geoderma, 269, 61–68, 2016.
Batjes, N. H., Ribeiro, E., and van Oostrum, A.: Standardised soil profile data to
support global mapping and modelling (WoSIS snapshot 2019), Earth Syst. Sci. Data, 12, 299–320,
https://doi.org/10.5194/essd-12-299-2020, 2020.
Bolin, B. and Rodhe, H.: A note on the concepts of age distribution and transit time in
natural reservoirs, Tellus, 25, 58–62, 1973.
Carvalhais, N., Reichstein, M., Seixas, J., Collatz, G. J., Pereira, J. S., Berbigier,
P., Carrara, A., Granier, A., Montagnani, L., and Papale, D.: Implications of the carbon cycle
steady state assumption for biogeochemical modeling performance and inverse parameter retrieval,
Global Biogeochem. Cy., 22, GB2007, https://doi.org/10.1029/2007GB003033, 2008.
Carvalhais, N., Forkel, M., Khomik, M., Bellarby, J., Jung, M., Migliavacca, M., Mu, M.,
Saatchi, S., Santoro, M., Thurner, M., Weber, U., Ahrens, B., Beer, C., Cescatti, A., Randerson,
J. T., and Reichstein, M.: Global covariation of carbon turnover times with climate in terrestrial
ecosystems, Nature, 514, 213–217, 2014.
Davidson, E. A. and Janssens, I. A.: Temperature sensitivity of soil carbon
decomposition and feedbacks to climate change, Nature, 440, 165–173, 2006.
Fan, N., Koirala, S., Reichstein, M., Thurner, M., Avitabile, V., Santoro, M., Ahrens,
B., Weber, U., and Carvarhais, N.: Ecosystem turnover time database, MPI-BGC,
https://doi.org/10.17871/bgitau.201911, 2019.
Fick, S. E. and Hijmans, R. J.: WorldClim 2: new 1-km spatial resolution climate
surfaces for global land areas, Int. J. Climatol., 37, 4302-4315, https://doi.org/10.1002/joc.5086, 2017.
Friedlingstein, P., Cox, P., Betts, R., Bopp, L., Von Bloh, W., Brovkin, V., Cadule, P., Doney, S., Eby, M., and Fung, I.: Climate–carbon cycle feedback analysis: results from the C4MIP model intercomparison, J. Climate, 19, 3337–3353, 2006.
Friend, A. D., Lucht, W., Rademacher, T. T., Keribin, R., Betts, R., Cadule, P., Ciais,
P., Clark, D. B., Dankers, R., and Falloon, P. D.: Carbon residence time dominates uncertainty in
terrestrial vegetation responses to future climate and atmospheric CO2,
P. Natl. Acad. Sci. USA, 111, 3280–3285, 2014.
Ge, R., He, H., Ren, X., Zhang, L., Yu, G., Smallman, T. L., Zhou, T., Yu, S. Y., Luo,
Y., and Xie, Z.: Underestimated ecosystem carbon turnover time and sequestration under the steady
state assumption: a perspective from long-term data assimilation, Glob. Change Biol., 25, 938-953,
https://doi.org/10.1111/gcb.14547, 2018.
Generation 3 Database Reports: Nave, L., Johnson, K., van Ingen, C., Agarwal, D.,
Humphrey, M., and Beekwilder, N: International Soil Carbon Network (ISCN) Database, Version 3,
Database Report: ISCN_SOC-DATA_LAYER_1-1, https://doi.org/10.17040/ISCN/1305039, 2017.
Hansen, N. and Ostermeier, A.: Completely Derandomized Self-Adaptation in Evolution
Strategies, Evol. Comput., 9, 159–195, https://doi.org/10.1162/106365601750190398, 2001.
Hengl, T., de Jesus, J. M., MacMillan, R. A., Batjes, N. H., Heuvelink, G. B.,
Ribeiro, E., Samuel-Rosa, A., Kempen, B., Leenaars, J. G., and Walsh, M. G.:
SoilGrids1km – global soil information based on automated mapping, PloS one, 9, e105992,
https://doi.org/10.1371/journal.pone.0105992, 2014.
Hengl, T., de Jesus, J. M., Heuvelink, G. B., Gonzalez, M. R., Kilibarda, M.,
Blagotić, A., Shangguan, W., Wright, M. N., Geng, X., and Bauer-Marschallinger, B.:
SoilGrids250m: Global gridded soil information based on machine learning, PloS one, 12, e0169748,
https://doi.org/10.1371/journal.pone.0169748, 2017.
Hugelius, G., Bockheim, J. G., Camill, P., Elberling, B., Grosse, G., Harden, J. W.,
Johnson, K., Jorgenson, T., Koven, C. D., Kuhry, P., Michaelson, G., Mishra, U., Palmtag, J.,
Ping, C.-L., O'Donnell, J., Schirrmeister, L., Schuur, E. A. G., Sheng, Y., Smith, L. C., Strauss,
J., and Yu, Z.: A new data set for estimating organic carbon storage to 3 m depth in soils of the
northern circumpolar permafrost region, Earth Syst. Sci. Data, 5, 393–402,
https://doi.org/10.5194/essd-5-393-2013, 2013.
Jackson, R. B., Lajtha, K., Crow, S. E., Hugelius, G., Kramer, M. G., and Piñeiro,
G.: The ecology of soil carbon: pools, vulnerabilities, and biotic and abiotic controls,
Annu. Rev. Ecol. Evol. Syst., 48, 419–445, https://doi.org/10.1146/annurev-ecolsys-112414-054234, 2017.
Jobbágy, E. G. and Jackson, R. B.: The vertical distribution of soil organic carbon
and its relation to climate and vegetation, Ecol. Appl., 10, 423–436, 2000.
Jung, M., Henkel, K., Herold, M., and Churkina, G.: Exploiting synergies of global land cover products for carbon cycle modeling, Remote Sens. Environ., 101, 534–553, 2006.
Jung, M., Reichstein, M., Schwalm, C. R., Huntingford, C., Sitch, S., Ahlström, A.,
Arneth, A., Camps-Valls, G., Ciais, P., and Friedlingstein, P.: Compensatory water effects link
yearly global land CO2 sink changes to temperature, Nature, 541, 516–520, 2017.
Jung, M., Schwalm, C., Migliavacca, M., Walther, S., Camps-Valls, G., Koirala, S., Anthoni, P., Besnard, S., Bodesheim, P., Carvalhais, N., Chevallier, F., Gans, F., Goll, D. S., Haverd, V., Köhler, P., Ichii, K., Jain, A. K., Liu, J., Lombardozzi, D., Nabel, J. E. M. S., Nelson, J. A., O'Sullivan, M., Pallandt, M., Papale, D., Peters, W., Pongratz, J., Rödenbeck, C., Sitch, S., Tramontana, G., Walker, A., Weber, U., and Reichstein, M.: Scaling carbon fluxes from eddy covariance sites to globe: synthesis and evaluation of the FLUXCOM approach, Biogeosciences, 17, 1343–1365, https://doi.org/10.5194/bg-17-1343-2020, 2020.
Koven, C. D., Hugelius, G., Lawrence, D. M., and Wieder, W. R.: Higher climatological
temperature sensitivity of soil carbon in cold than warm climates, Nat. Clim. Change, 7, 817–822,
https://doi.org/10.1038/nclimate3421, 2017.
Padarian, J., Minasny, B., and McBratney, A. B.: Using deep learning for digital soil
mapping, SOIL, 5, 79–89, https://doi.org/10.5194/soil-5-79-2019, 2019.
Raich, J. and Schlesinger, W. H.: The global carbon dioxide flux in soil respiration
and its relationship to vegetation and climate, Tellus B, 44, 81–99,
https://doi.org/10.3402/tellusb.v44i2.15428, 1992.
Saatchi, S. S., Harris, N. L., Brown, S., Lefsky, M., Mitchard, E. T., Salas, W.,
Zutta, B. R., Buermann, W., Lewis, S. L., and Hagen, S.: Benchmark map of forest carbon stocks in
tropical regions across three continents, P. Natl. Acad. Sci. USA, 108, 9899–9904, 2011.
Sanderman, J., Hengl, T., and Fiske, G. J.: Soil carbon debt of 12,000 years of human
land use, P. Natl. Acad. Sci. USA, 114, 9575–9580, 2017.
Santoro, M., Beaudoin, A., Beer, C., Cartus, O., Fransson, J. E., Hall, R. J., Pathe,
C., Schmullius, C., Schepaschenko, D., and Shvidenko, A.: Forest growing stock volume of the
northern hemisphere: Spatially explicit estimates for 2010 derived from Envisat ASAR, Remote
Sens. Environ., 168, 316–334, 2015.
Santoro, M., Cartus, O., Mermoz, S., Bouvet, A., Le Toan, T., Carvalhais, N.,
Rozendaal, D., Herold, M., Avitabile, V., Quegan, S., Carreiras, J., Rauste, Y., Balzter, H.,
Schmullius, C., Seifert, F. M.: A detailed portrait of the forest aboveground biomass pool for the
year 2010 obtained from multiple remote sensing observations, 20th EGU General Assembly, EGU2018,
Proceedings from the conference held 4–13 April 2018 in Vienna, Austria, p. 18932, available at:
https://ui.adsabs.harvard.edu/abs/2018EGUGA..2018932S/abstract (last access: 5 August 2019), 2018.
Santoro, M., Cartus, O., Carvalhais, N., Rozendaal, D., Avitabilie, V., Araza, A., de Bruin, S., Herold, M., Quegan, S., Rodríguez Veiga, P., Balzter, H., Carreiras, J., Schepaschenko, D., Korets, M., Shimada, M., Itoh, T., Moreno Martínez, Á., Cavlovic, J., Cazzolla Gatti, R., da Conceição Bispo, P., Dewnath, N., Labrière, N., Liang, J., Lindsell, J., Mitchard, E. T. A., Morel, A., Pacheco Pascagaza, A. M., Ryan, C. M., Slik, F., Vaglio Laurin, G., Verbeeck, H., Wijaya, A., and Willcock, S.: The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations, Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2020-148, in review, 2020.
Thurner, M., Beer, C., Santoro, M., Carvalhais, N., Wutzler, T., Schepaschenko, D.,
Shvidenko, A., Kompter, E., Ahrens, B., and Levick, S. R.: Carbon stock and density of northern
boreal and temperate forests, Global Ecol. Biogeogr., 23, 297–310, https://doi.org/10.1111/geb.12125, 2014.
Thurner, M., Beer, C., Ciais, P., Friend, A. D., Ito, A., Kleidon, A., Lomas, M. R., Quegan, S., Rademacher, T. T., and Schaphoff, S.: Evaluation of climate‐related carbon turnover processes in global vegetation models for boreal and temperate forests, Global Change Biol., 23, 3076–3091, https://doi.org/10.1111/gcb.13660, 2017.
Tifafi, M., Guenet, B., and Hatté, C.: Large differences in global and regional
total soil carbon stock estimates based on SoilGrids, HWSD, and NCSCD: Intercomparison and
evaluation based on field data from USA, England, Wales, and France, Global Biogeochem. Cy., 32,
42–56, https://doi.org/10.1002/2017GB005678, 2018.
Todd-Brown, K. E. O., Randerson, J. T., Post, W. M., Hoffman, F. M., Tarnocai, C.,
Schuur, E. A. G., and Allison, S. D.: Causes of variation in soil carbon simulations from CMIP5
Earth system models and comparison with observations, Biogeosciences, 10, 1717–1736,
https://doi.org/10.5194/bg-10-1717-2013, 2013.
Todd-Brown, K. E. O., Randerson, J. T., Hopkins, F., Arora, V., Hajima, T., Jones, C.,
Shevliakova, E., Tjiputra, J., Volodin, E., Wu, T., Zhang, Q., and Allison, S. D.: Changes in soil
organic carbon storage predicted by Earth system models during the 21st century, Biogeosciences,
11, 2341–2356, https://doi.org/10.5194/bg-11-2341-2014, 2014.
Tramontana, G., Jung, M., Schwalm, C. R., Ichii, K., Camps-Valls, G., Ráduly, B.,
Reichstein, M., Arain, M. A., Cescatti, A., Kiely, G., Merbold, L., Serrano-Ortiz, P., Sickert,
S., Wolf, S., and Papale, D.: Predicting carbon dioxide and energy fluxes across global FLUXNET
sites with regression algorithms, Biogeosciences, 13, 4291–4313,
https://doi.org/10.5194/bg-13-4291-2016, 2016.
Wadoux, A. M. J.-C., Padarian, J., and Minasny, B.: Multi-source data integration for soil mapping using deep learning, SOIL, 5, 107–119, https://doi.org/10.5194/soil-5-107-2019, 2019.
Wang, J., Sun, J., Xia, J., He, N., Li, M., and Niu, S.: Soil and vegetation carbon
turnover times from tropical to boreal forests, Funct. Ecol., 32, 71–82,
https://doi.org/10.1111/1365-2435.12914, 2018.
Webb, R., Rosenzweig, C. E., and Levine, E. R.: Global Soil Texture and Derived
Water-Holding Capacities, ORNL DAAC, Oak Ridge, TN, USA, https://doi.org/10.3334/ORNLDAAC/548, 2000.
Wenzel, S., Cox, P. M., Eyring, V., and Friedlingstein, P.: Emergent constraints on
climate-carbon cycle feedbacks in the CMIP5 Earth system models, J. Geophys. Res.-Biogeo., 119,
794–807, https://doi.org/10.1002/2013JG002591, 2014.
Wheeler, I., Hengl, T.: Soil organic carbon stock in kg∕m2 time-series
2001–2015 based on the land cover changes (Version v0.2) [Data set], Zenodo,
https://doi.org/10.5281/zenodo.2529721, 2018.
Zhang, Y., Xiao, X., Wu, X., Zhou, S., Zhang, G., Qin, Y., and Dong, J.: A global
moderate resolution dataset of gross primary production of vegetation for 2000–2016, Sci. Data,
4, 170165, https://doi.org/10.1038/sdata.2017.165, 2017.
Short summary
The turnover time of terrestrial carbon (τ) controls the global carbon cycle–climate feedback. In this study, we provide a new, updated ensemble of diagnostic terrestrial carbon turnover times and associated uncertainties on a global scale. Despite the large variation in both magnitude and spatial patterns of τ, we identified robust features in the spatial patterns of τ which could contribute to uncertainty reductions in future projections of the carbon cycle–climate feedback.
The turnover time of terrestrial carbon (τ) controls the global carbon cycle–climate feedback....
Altmetrics
Final-revised paper
Preprint